Enlargement, subdivision and individualization of statistical shape models: Application to 3D medical image segmentation
This thesis presents three original and complementary approaches to enhance the quality of Statistical Shape Models (SSMs), that improve the accuracy of medical image segmentation in challenging applications. First, we enhance the statistical richness of SSMs by developing a technique capable of mer...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2017 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/441754 |
| Acceso en línea: | http://hdl.handle.net/10803/441754 |
| Access Level: | acceso abierto |
| Palabra clave: | Active Shape Models Cardiac magnetic resonance Computed tomography Conditional models Magnetic resonance imaging Model fusion Patient metadata Personalized medicine Statistical shape models Vertebral segmentation Modelos de forma activa Resonancia cardiaca del corazón Tomografía computarizada Modelos condicionales Imagen de resonancia magnética Fusion de modelos Metadatos del paciente Medicina personalizada Modelos estadísticos de forma Segmentación vertebras 62 |
| Sumario: | This thesis presents three original and complementary approaches to enhance the quality of Statistical Shape Models (SSMs), that improve the accuracy of medical image segmentation in challenging applications. First, we enhance the statistical richness of SSMs by developing a technique capable of merging the shape representations and statistical properties of several pre-existing models with no original or additional raw data. Second, we enhance the geometrical quality of SSMs by developing a framework for modeling simultaneously both global and local characteristics of highly complex and/or multi-part anatomical shapes. Last, we improve the specificity of SSMs for specific subjects by integrating individual-specific non-imaging metadata such as demographic, clinical and behavioral variables into the SSM construction and image segmentation tasks. These techniques are demonstrated and validated by considering various imaging modalities such as magnetic resonance imaging (MRI) and computed tomography (CT), and different complex anatomies, including the human heart, brain and spine. |
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